Impact of sleep disorder treatment on fatigue in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: We recently reported that sleep disorders are significantly associated with fatigue in multiple sclerosis (MS). OBJECTIVE: The objective of this paper is to assess the effects of sleep disorder treatment on fatigue and related clinical outcomes in MS. METHODS: This was a controlled, non-randomized clinical treatment study. Sixty-two MS patients completed standardized questionnaires including the Fatigue Severity Scale (FSS), Multidimensional Fatigue Inventory (MFI), Epworth Sleepiness scale (ESS) and Pittsburgh Sleep Quality Index (PSQI), and underwent polysomnography (PSG). Patients with sleep disorders were offered standard treatment. Fifty-six subjects repeated the questionnaires after ≥ three months, and were assigned to one of three groups: sleep disorders that were treated (SD-Tx, n=21), sleep disorders remaining untreated (SD-NonTx, n=18) and no sleep disorder (NoSD, n=17). RESULTS: FSS and MFI general and mental fatigue scores improved significantly from baseline to follow-up in SD-Tx (p <0.03), but not SD-NonTx or NoSD subjects. ESS and PSQI scores also improved significantly in SD-Tx subjects (p <0.001). Adjusted multivariate analyses confirmed significant effects of sleep disorder treatment on FSS (-0.87, p = 0.005), MFI general fatigue score (p = 0.034), ESS (p = 0.042) and PSQI (p = 0.023). CONCLUSION: Treatment of sleep disorders can improve fatigue and other clinical outcomes in MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".